Dr. Jörg Zimmermann
I am currently working on the foundations of machine learning and the application of evolutionary algorithms to Real World learning problems. The search for foundations of machine learning leads to three main questions:
In the long run, a general learning system should be able to detect as many regularities in its percept stream as possible, while dealing sensibly with the inherent uncertainty of predictions based on a finite amount of data.
Jörg Zimmermann and Armin B. Cremers:
Machine Spaces: Axioms and Metrics,
T. Neary, M. Cook (Eds.): Proceedings of Machines, Computations and Universality (MCU 2013),
pp. 33-34, EPTCS 128, doi:10.4204/EPTCS.128, 2013
MCU 2013 Talk: Machine Spaces: Axioms and Metrics [Slides]
Jörg Zimmermann and Armin B. Cremers:
Making Solomonoff Induction Effective or You Can Learn What You Can Bound,
S. B. Cooper, A. Dawar, B. Löwe (Eds.): How the World Computes,
pp. 745-754, LNCS 7318, Proceedings of the CiE 2012, Turing Centenary
Conference, Springer, 2012.
The original publication is available at www.springerlink.com
Turing Centenary Conference Talk: Making Solomonoff Induction Effective [Slides]
Jörg Zimmermann and Armin B. Cremers: The Quest for Uncertainty, C. Calude, G. Rozenberg, A. Salomaa (Eds.): Rainbow of Computer Science, pp. 270-283, Springer, 2011. The original publication is available at www.springerlink.com [Link] [PDF]
Jörg Zimmermann, Robin Höns, Heinz Mühlenbein: From Theory to Practice: An Evolutionary Algorithm for the Antenna Placement Problem, S. Tsutsui, A. Ghosh (Eds.): Advances in Evolutionary Computation, pp. 713-737, Springer, 2003
Jörg Zimmermann, Robin Höns, Heinz Mühlenbein: ENCON: An Evolutionary Algorithm for the Antenna Placement Problem, Computers & Industrial Engineering, 44(2): 209-226, 2003
Frank Schweitzer, Jörg Zimmermann, Heinz Mühlenbein: Coordination of Decisions in a spatial agent model, Physica A, 303: 189-216, 2002 [Link]
Frank Schweitzer and Jörg Zimmermann: Communication and Self-Organisation in Complex Systems: A Basic Approach, M. M. Fischer, J. Fröhlich (Eds.): Knowledge, Complexity and Innovation Systems, pp. 275-296, Springer, 2001
Heinz Mühlenbein and Jörg Zimmermann: Size of Neighborhood More Important than Temperature for Stochastic Local Search, Proceedings of the Congress on Evolutionary Computation (CEC), 2000 [PDF]
Jörg Zimmermann, Robin Höns, Heinz Mühlenbein: The Antenna Placement Problem - An Evolutionary Approach, B. Gavish (Ed.): 8th International Conference on Telecommunication Systems, pp. 358-366, 2000 [PDF]
The question of how to represent and process uncertainty is of fundamental importance to the scientific process, but also in everyday life. Currently there exist a lot of different calculi for managing uncertainty, each having its own advantages and disadvantages. Especially, almost all are defining the domain and structure of uncertainty values a priori, e.g., one real number, two real numbers, a finite domain, and so on, but maybe uncertainty is best measured by complex numbers, matrices or still another mathematical structure. This thesis investigates the notion of uncertainty from a foundational point of view, provides an ontology and axiomatic core system for uncertainty and derives and not defines the structure of uncertainty. The main result, the ring theorem, stating that uncertainty values are elements of the [0,1]-interval of a partially ordered ring, is used to derive a general decomposition theorem for uncertainty values, splitting them into a numerical interval and an "interaction term". In order to illustrate the unifying power of these results, the relationship to Dempster-Shafer theory is discussed and it is shown that all Dempster-Shafer measures over finite domains can be represented by ring-valued uncertainty measures. Finally, the historical development of approaches to modeling uncertainty which have led to the results of this thesis are reviewed.